Controlling sensitivity and stability of ferrous–xylenol orange–gelatin 3D gel dosimeters by doping with phenanthroline-type ligands and glyoxal
Bibliographic record
Abstract
The ferrous-xylenol orange-gelatin (FXG) dosimeter is widely used for three-dimensional ionizing radiation field mapping through optical scanning. Upon irradiation, the ferrous iron (Fe(2+)) is oxidized to ferric iron (Fe(3+)), which forms an intensely coloured complex with xylenol orange (XO). XO also acts as a diffusion-limiting additive; however, its presence may cause rapid auto-oxidation of Fe(2+) during storage and low stability of the dose response. In this work, phenanthroline-type ligands were added to FXG system in a bid to bind the ferrous iron in a stable complex and minimize the rate of the auto-oxidation, whereas glyoxal was used as a chemical cross-linker, aiming to minimize the ferric iron diffusion. It was found that addition of either 1,10-phenanthroline or 5-nitro-1,10-phenanthroline can improve the auto-oxidation behaviour of the gels. However, the initial background absorbance was slightly increased, and the sensitivity of the dosimeters was decreased. Doping with glyoxal led to a moderate decrease of the diffusion only in those gels that also contained a phenanthroline-type ligand, and did not affect the initial dose response. Glyoxal also afforded an extended period of stable background absorbance level after an initial period of bleaching of the gel. Following re-irradiation, most glyoxal-containing dosimeters showed an excellent linearity of the dose response, albeit at a decreased sensitivity. We recommend further testing of FXG dosimeters, doped with phenanthroline-type ligands and glyoxal as a means for controlling the dose response and improving the long-term storage properties of the gels and the potential for dose fractionation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".